Predicting the Percent of Agricultural Land in Different Countries

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I. Introduction

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Table 1.Sample for 5 randomly chosen countries of the data set used in this study

Country	agricultural_land_p_2016	forest_area_p_2015	population_growth_p_2015
Georgia	34.45100	40.61591	0.1574814
Pacific island small states	13.43769	61.56636	1.3132995
Nauru	20.00000	0.00000	5.1145642
Indonesia	31.46442	50.23819	1.2674656
Cyprus	12.15693	18.69048	0.7521855

Country	aded_val_GDP_2015	perm_cropLand_p_2015
Georgia	7.813762	1.582962
Pacific island small states	11.906936	5.961059
Nauru	4.196069	20.000000
Indonesia	13.492644	12.420166
Cyprus	1.874272	2.857143

II. Exploratory data analysis

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Table 2: Summary for the percent of agricultural land in different countries, in 2016

n	min	median	mean	max	sd
225	0.5576923	39.275	38.90696	82.55971	19.67133

Figure 1. Distribution for the percent of agricultural land in different countries, in 2016

Figure 4. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their 2015 Permanent Crop Land (% of land area). The red line is the best fit line. The blue curve is the Loess curve.

Figure 5. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their percent of forest area, in 2015. The red line is the best fit line. The blue curve is the Loess curve.

Figure 8. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their percent annual population growth in 2015. The red line is the best fit line. The blue curve is the Loess curve.

Figure 9. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against the % added value of Agriculture, forestry, and fishing to their GDP in 2015. The red line is the best fit line. The blue curve is the Loess curve.

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III. Multiple linear regression

i. Methods

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The model is:

## lm(formula = agricultural_land_p_2016 ~ perm_cropLand_p_2015 + 
##     ns(population_growth_p_2015, df = 4) + ns(forest_area_p_2015, 
##     df = 4) + ns(aded_val_GDP_2015, df = 4), data = tidy_joined_dataset)

Figure 10. Normal Q-Qplot for the percent of agricultural land in different countries, in 2016

Table 3: VIF table

	GVIF	Df	GVIF^(1/(2*Df))
perm_cropLand_p_2015	1.124085	1	1.060229
ns(population_growth_p_2015, df = 4)	2.421891	4	1.116913
ns(forest_area_p_2015, df = 4)	1.490688	4	1.051171
ns(aded_val_GDP_2015, df = 4)	2.007460	4	1.091015

ii. Model Results and Interpretation

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## lm(formula = agricultural_land_p_2016 ~ perm_cropLand_p_2015 + 
##     ns(population_growth_p_2015, df = 4) + ns(forest_area_p_2015, 
##     df = 4) + ns(aded_val_GDP_2015, df = 4), data = tidy_joined_dataset)

Table 4. Model Summary Table

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	24.1215	10.3841	2.3229	0.0211
perm_cropLand_p_2015	0.4914	0.1434	3.4269	0.0007
ns(population_growth_p_2015, df = 4)1	-9.6982	8.5759	-1.1309	0.2594
ns(population_growth_p_2015, df = 4)2	10.3611	7.9389	1.3051	0.1933
ns(population_growth_p_2015, df = 4)3	-20.9933	19.8490	-1.0576	0.2914
ns(population_growth_p_2015, df = 4)4	-42.8759	11.5787	-3.7030	0.0003
ns(forest_area_p_2015, df = 4)1	-0.5732	4.3938	-0.1305	0.8963
ns(forest_area_p_2015, df = 4)2	-19.1007	5.0858	-3.7557	0.0002
ns(forest_area_p_2015, df = 4)3	2.6878	9.8495	0.2729	0.7852
ns(forest_area_p_2015, df = 4)4	-49.7971	7.6792	-6.4847	0.0000
ns(aded_val_GDP_2015, df = 4)1	15.5354	4.4557	3.4866	0.0006
ns(aded_val_GDP_2015, df = 4)2	7.3391	6.7614	1.0854	0.2790
ns(aded_val_GDP_2015, df = 4)3	42.0494	10.6767	3.9384	0.0001
ns(aded_val_GDP_2015, df = 4)4	13.8894	11.0561	1.2563	0.2104

	Value	df
Residual Standard Error	14.926	211
Multiple R-squared	0.458
Adjusted R-squared	0.424

	Value	Numerator df	Denominator df
Model F-statistic	13.7	13	211
P-value	7.366e-22

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iii. Inference for multiple regression

Table 5. ANOVA (Type II tests) Table

	Sum Sq	Df	F value	Pr(>F)
perm_cropLand_p_2015	2616.434	1	11.7435	0.0007
ns(population_growth_p_2015, df = 4)	3295.916	4	3.6983	0.0062
ns(forest_area_p_2015, df = 4)	27741.579	4	31.1287	0.0000
ns(aded_val_GDP_2015, df = 4)	4420.418	4	4.9601	0.0008
Residuals	47010.294	211	NA	NA

Figure 13. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their 2015 Permanent Crop Land (% of land area), where the forest area percentage of a country’s land in 2015 euqals its median = 31.88387, for a median percent population growth in a country in 2015 = 1.256186, and for a median % added value of agriculture, forestry, and fishing to the GDP of a country = 7.408892. The blue line is the linear curve, with its associated 95% CI and wider pink 95% PI.

Figure 14. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their percent annual population growth in 2015, where the forest area percentage of a country’s land in 2015 euqals its median = 31.88387, for a median % added value of agriculture, forestry, and fishing to the GDP of a country = 7.408892, and for a median percent of permanent crop land of a country’s land area = 1.311853. The blue line is the natural spline, with its associated 95% CI and wider pink 95% PI.

Figure 15. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their percent of forest area, in 2015, for a median percent population growth in a country in 2015 = 1.256186, for a median % added value of agriculture, forestry, and fishing to the GDP of a country = 7.408892, and for a median percent of permanent crop land of a country’s land area = 1.311853. The blue line is the natural spline, with its associated 95% CI and wider pink 95% PI.

Figure 16. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against the % added value of Agriculture, forestry, and fishing to their GDP in 2015, where the forest area percentage of a country’s land in 2015 euqals its median = 31.88387, for a median percent population growth in a country in 2015 = 1.256186, and for a median percent of permanent crop land of a country’s land area = 1.311853. The blue line is the natural spline, with its associated 95% CI and wider pink 95% PI.

IV. Discussion

i. Conclusions

ii. Limitations

iii. Further questions

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V. Citations and References